library(DESeq2)
library(bcbioRNASeq)

# Shared R Markdown settings
prepareRNASeqTemplate()
if (file.exists("setup.R")) {
    source("setup.R")
}

# Directory paths
dataDir <- file.path(params$outputDir, "data")
countsDir <- file.path(params$outputDir, "results", "counts")
deDir <- file.path(params$outputDir, "results", "differential_expression")

# Load bcbioRNASeq object
bcbName <- load(params$bcbFile)
bcb <- get(bcbName, inherits = FALSE)

Overview

  • Principal Investigator:
  • Researcher:
  • Experiment:

> dds <- bcbio(bcb, "DESeqDataSet")
> design(dds) <- params$design
> dds <- DESeq(dds)
> rld <- rlog(dds)

Alpha level (FDR) cutoffs

Let’s take a look at the number of genes we get with different false discovery rate (FDR) cutoffs. These tests subset P values that have been multiple test corrected using the Benjamini Hochberg (BH) method (Benjamini and Hochberg 1995).

> alphaSummary(dds)
0.1 0.05 0.01 0.001 1e-06
LFC > 0 (up) 449, 2.3% 293, 1.5% 98, 0.49% 34, 0.17% 6, 0.03%
LFC < 0 (down) 260, 1.3% 150, 0.75% 36, 0.18% 22, 0.11% 5, 0.025%
outliers 36, 0.18% 36, 0.18% 36, 0.18% 36, 0.18% 36, 0.18%
low counts 6851, 34% 8369, 42% 9131, 46% 0, 0% 13698, 69%
cutoff (mean count < 20) (mean count < 41) (mean count < 55) (mean count < 0) (mean count < 212)

Results

> # help("results", "DESeq2")
> # For contrast argument as character vector:
> #   1. Design matrix factor of interest.
> #   2. Numerator for LFC (expt).
> #   3. Denominator for LFC (control).
> resUnshrunken <- results(
+     dds,
+     contrast = params$contrast,
+     alpha = params$alpha)
> 
> # DESeqResults with shrunken log2 fold changes (LFC)
> # help("lfcShrink", "DESeq2")
> # Only `coef` or `contrast` can be specified, not both
> # Use the correct `coef` number to modify from `resultsNames(dds)`
> resShrunken <- lfcShrink(
+     dds = dds,
+     # coef = 2,
+     contrast = params$contrast,
+     res = resUnshrunken)
> 
> # Use shrunken LFC values by default
> res <- resShrunken
> saveData(res, dir = dataDir)

We performed the analysis using a BH adjusted P value cutoff of 0.05 and a log fold-change (LFC) ratio cutoff of 1.

Plots

Mean average (MA)

An MA plot compares transformed counts on M (log ratio) and A (mean average) scales (Y. H. Yang et al. 2002).

> plotMA(res)

Volcano

A volcano plot compares significance (BH-adjusted P value) against fold change (log2) (Cui and Churchill 2003; Li et al. 2014). Genes in the green box with text labels have an adjusted P value are likely to be the top candidate genes of interest.

> plotVolcano(res, lfc = params$lfc)

Heatmap

This plot shows only differentially expressed genes on a per-sample basis. We have scaled the data by row and used the ward.D2 method for clustering (Ward 1963).

> plotDEGHeatmap(res, counts = rld)

> top50res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:50, ]
> top50gene <- row.names(top50res)
> 
> plotHeatmap(bcb, top50gene, normalized = "rlog")

File downloads

The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.

Count matrices

  • normalizedCounts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.
  • tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.
  • rawCounts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.

Differentially expressed genes (DEG)

> resTbl <- resultsTables(res, lfc = params$lfc, write = TRUE, summary = TRUE, 
+     headerLevel = 3, dir = deDir)

Summary statistics

  • 25168 genes in count matrix
  • base mean > 0: 19949 genes (non-zero)
  • base mean > 1: 17643 genes
  • alpha cutoff: 0.05
  • lfc cutoff: 1 (applied in tables only)
  • deg pass alpha: 443 genes
  • deg lfc up: 10 genes
  • deg lfc down: 16 genes

DEG tables are sorted by BH-adjusted P value, and contain the following columns:

  • ensgene: Ensembl gene identifier.
  • baseMean: Mean of the normalized counts per gene for all samples.
  • log2FoldChange: log2 fold change.
  • lfcSE: log2 standard error.
  • stat: Wald statistic.
  • pvalue: Walt test P value.
  • padj: BH adjusted Wald test P value (corrected for multiple comparisons; aka FDR).
  • externalGeneName: Ensembl name (a.k.a. symbol).
  • description: Ensembl description.
  • geneBiotype: Ensembl biotype (e.g. protein_coding).

Top tables

Only the top up- and down-regulated genes (arranged by log2 fold change) are shown.

> topTables(resTbl)
sampleclass lung distaltumor vs lung notumor (upregulated)
ensgene baseMean lfc padj symbol description
ENSMUSG00000038418 452 1.50 6.86e-17 Egr1 early growth response 1
ENSMUSG00000021250 400 1.69 9.26e-15 Fos FBJ osteosarcoma oncogene
ENSMUSG00000028859 254 1.57 8.82e-08 Csf3r colony stimulating factor 3 receptor (granulocyte)
ENSMUSG00000021134 1352 1.01 8.99e-08 Srsf5 serine/arginine-rich splicing factor 5
ENSMUSG00000023034 410 1.31 1.80e-06 Nr4a1 nuclear receptor subfamily 4, group A, member 1
ENSMUSG00000056054 111 1.19 1.64e-05 S100a8 S100 calcium binding protein A8 (calgranulin A)
ENSMUSG00000027398 94 1.37 8.09e-05 Il1b interleukin 1 beta
ENSMUSG00000070000 48 1.12 3.58e-04 Fcho1 FCH domain only 1
ENSMUSG00000021624 53 1.08 2.39e-03 Cd180 CD180 antigen
ENSMUSG00000021209 124 1.07 4.15e-03 Ppp4r4 protein phosphatase 4, regulatory subunit 4
sampleclass lung distaltumor vs lung notumor (downregulated)
ensgene baseMean lfc padj symbol description
ENSMUSG00000097554 3743 -1.30 5.81e-12 Gm26825 predicted gene, 26825
ENSMUSG00000001774 287 -1.15 1.44e-10 Chordc1 cysteine and histidine-rich domain (CHORD)-containing, zinc-binding protein 1
ENSMUSG00000022206 1295 -1.12 1.44e-10 Npr3 natriuretic peptide receptor 3
ENSMUSG00000066687 1192 -1.55 2.49e-10 Zbtb16 zinc finger and BTB domain containing 16
ENSMUSG00000003949 665 -1.07 7.05e-08 Hlf hepatic leukemia factor
ENSMUSG00000015656 2650 -1.16 2.08e-07 Hspa8 heat shock protein 8
ENSMUSG00000024966 510 -1.25 3.63e-06 Stip1 stress-induced phosphoprotein 1
ENSMUSG00000020288 200 -1.05 2.02e-05 Ahsa2 AHA1, activator of heat shock protein ATPase 2
ENSMUSG00000004951 1058 -1.00 2.08e-05 Hspb1 heat shock protein 1
ENSMUSG00000024222 201 -1.27 3.29e-05 Fkbp5 FK506 binding protein 5
ENSMUSG00000021131 100 -1.06 5.31e-05 Erh enhancer of rudimentary homolog (Drosophila)
ENSMUSG00000028410 1389 -1.12 5.31e-05 Dnaja1 DnaJ heat shock protein family (Hsp40) member A1
ENSMUSG00000053930 67 -1.15 3.58e-04 Shisa6 shisa family member 6
ENSMUSG00000029657 889 -1.03 2.84e-03 Hsph1 heat shock 105kDa/110kDa protein 1
ENSMUSG00000060636 334 -1.06 2.88e-03 Rpl35a ribosomal protein L35A
ENSMUSG00000050855 60 -1.06 8.48e-03 Zfp940 zinc finger protein 940

Expression patterns

> top10res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:10, ]
> top10gene <- row.names(top10res)
> 
> plots <- plotGene(bcb, top10gene, returnList = TRUE)
> 
> n = 1
> cat(paste("##", plots[[n]]$labels$title))

Egr1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Fos

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Gm26825

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Chordc1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Npr3

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Zbtb16

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Hlf

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Csf3r

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Srsf5

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Hspa8

> plots[[n]]


Methods

RNA-seq counts were generated by bcbio and bcbioRNASeq using salmon (Patro et al. 2017). Counts were imported into R using tximport (Soneson, Love, and Robinson 2016) and DESeq2 (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015).

R session information

> mdHeader("`devtools::session_info()`", level = 2)

devtools::session_info()

> devtools::session_info()
##  setting  value                       
##  version  R version 3.4.3 (2017-11-30)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/New_York            
##  date     2018-02-01                  
## 
##  package                * version   date      
##  acepack                  1.4.1     2016-10-29
##  affy                     1.56.0    2017-10-31
##  affyio                   1.48.0    2017-10-31
##  annotate                 1.56.1    2017-11-13
##  AnnotationDbi            1.40.0    2017-10-31
##  AnnotationFilter         1.2.0     2017-10-31
##  AnnotationHub            2.10.1    2017-11-08
##  assertthat               0.2.0     2017-04-11
##  backports                1.1.2     2017-12-13
##  base                   * 3.4.3     2017-12-07
##  base64enc                0.1-3     2015-07-28
##  basejump                 0.2.0     2018-01-29
##  bcbioBase              * 0.0.3     2018-01-29
##  bcbioRNASeq            * 0.1.4     2018-01-30
##  bindr                    0.1       2016-11-13
##  bindrcpp               * 0.2       2017-06-17
##  Biobase                * 2.38.0    2017-10-31
##  BiocGenerics           * 0.24.0    2017-10-31
##  BiocInstaller            1.28.0    2017-10-31
##  BiocParallel             1.12.0    2017-10-31
##  biomaRt                  2.34.2    2018-01-20
##  Biostrings               2.46.0    2017-10-31
##  bit                      1.1-12    2014-04-09
##  bit64                    0.9-7     2017-05-08
##  bitops                   1.0-6     2013-08-17
##  blob                     1.1.0     2017-06-17
##  broom                    0.4.3     2017-11-20
##  cellranger               1.1.0     2016-07-27
##  checkmate                1.8.5     2017-10-24
##  circlize                 0.4.3     2017-12-20
##  cli                      1.0.0     2017-11-05
##  cluster                  2.0.6     2017-03-10
##  codetools                0.2-15    2016-10-05
##  colorspace               1.3-2     2016-12-14
##  compiler                 3.4.3     2017-12-07
##  ComplexHeatmap           1.17.1    2017-10-25
##  ConsensusClusterPlus     1.42.0    2017-10-31
##  cowplot                  0.9.2     2017-12-17
##  crayon                   1.3.4     2017-09-16
##  curl                     3.1       2017-12-12
##  data.table               1.10.4-3  2017-10-27
##  datasets               * 3.4.3     2017-12-07
##  DBI                      0.7       2017-06-18
##  DEGreport              * 1.14.1    2017-12-19
##  DelayedArray           * 0.4.1     2017-11-07
##  dendsort                 0.3.3     2015-12-14
##  DESeq2                 * 1.18.1    2017-11-12
##  devtools                 1.13.4    2017-11-09
##  digest                   0.6.15    2018-01-28
##  dplyr                  * 0.7.4     2017-09-28
##  edgeR                    3.20.7    2018-01-18
##  ensembldb                2.2.0     2017-10-31
##  evaluate                 0.10.1    2017-06-24
##  forcats                * 0.2.0     2017-01-23
##  foreign                  0.8-69    2017-06-22
##  formatR                  1.5       2017-04-25
##  Formula                  1.2-2     2017-07-10
##  genefilter               1.60.0    2017-10-31
##  geneplotter              1.56.0    2017-10-31
##  GenomeInfoDb           * 1.14.0    2017-10-31
##  GenomeInfoDbData         1.0.0     2018-01-29
##  GenomicAlignments        1.14.1    2017-11-18
##  GenomicFeatures          1.30.1    2018-01-26
##  GenomicRanges          * 1.30.1    2017-12-21
##  GetoptLong               0.1.6     2017-03-07
##  ggplot2                * 2.2.1     2016-12-30
##  ggrepel                  0.7.0     2017-09-29
##  GlobalOptions            0.0.12    2017-05-21
##  glue                     1.2.0     2017-10-29
##  graphics               * 3.4.3     2017-12-07
##  grDevices              * 3.4.3     2017-12-07
##  grid                     3.4.3     2017-12-07
##  gridExtra                2.3       2017-09-09
##  grr                      0.9.5     2016-08-26
##  gtable                   0.2.0     2016-02-26
##  haven                    1.1.1     2018-01-18
##  highr                    0.6       2016-05-09
##  Hmisc                    4.1-1     2018-01-03
##  hms                      0.4.1     2018-01-24
##  htmlTable                1.11.2    2018-01-20
##  htmltools                0.3.6     2017-04-28
##  htmlwidgets              1.0       2018-01-20
##  httpuv                   1.3.5     2017-07-04
##  httr                     1.3.1     2017-08-20
##  interactiveDisplayBase   1.16.0    2017-10-31
##  IRanges                * 2.12.0    2017-10-31
##  jsonlite                 1.5       2017-06-01
##  knitr                  * 1.19      2018-01-29
##  labeling                 0.3       2014-08-23
##  lattice                  0.20-35   2017-03-25
##  latticeExtra             0.6-28    2016-02-09
##  lazyeval                 0.2.1     2017-10-29
##  limma                    3.34.6    2018-01-24
##  locfit                   1.5-9.1   2013-04-20
##  logging                  0.7-103   2013-04-12
##  lubridate                1.7.1     2017-11-03
##  magrittr                 1.5       2014-11-22
##  Matrix                   1.2-12    2017-11-20
##  Matrix.utils             0.9.6     2017-08-28
##  MatrixModels             0.4-1     2015-08-22
##  matrixStats            * 0.53.0    2018-01-24
##  memoise                  1.1.0     2017-04-21
##  methods                * 3.4.3     2017-12-07
##  mime                     0.5       2016-07-07
##  mnormt                   1.5-5     2016-10-15
##  modelr                   0.1.1     2017-07-24
##  munsell                  0.4.3     2016-02-13
##  nlme                     3.1-131   2017-02-06
##  nnet                     7.3-12    2016-02-02
##  Nozzle.R1                1.1-1     2013-05-15
##  parallel               * 3.4.3     2017-12-07
##  pheatmap                 1.0.8     2015-12-11
##  pillar                   1.1.0     2018-01-14
##  pkgconfig                2.0.1     2017-03-21
##  plyr                     1.8.4     2016-06-08
##  preprocessCore           1.40.0    2017-10-31
##  prettyunits              1.0.2     2015-07-13
##  progress                 1.1.2     2016-12-14
##  ProtGenerics             1.10.0    2017-10-31
##  psych                    1.7.8     2017-09-09
##  purrr                  * 0.2.4     2017-10-18
##  quantreg               * 5.34      2017-10-25
##  R.methodsS3              1.7.1     2016-02-16
##  R.oo                     1.21.0    2016-11-01
##  R.utils                  2.6.0     2017-11-05
##  R6                       2.2.2     2017-06-17
##  RColorBrewer             1.1-2     2014-12-07
##  Rcpp                     0.12.15   2018-01-20
##  RCurl                    1.95-4.10 2018-01-04
##  readr                  * 1.1.1     2017-05-16
##  readxl                   1.0.0     2017-04-18
##  reshape                  0.8.7     2017-08-06
##  reshape2                 1.4.3     2017-12-11
##  rjson                    0.2.15    2014-11-03
##  rlang                    0.1.6     2017-12-21
##  rmarkdown                1.8       2017-11-17
##  RMySQL                   0.10.13   2017-08-14
##  rpart                    4.1-12    2018-01-12
##  rprojroot                1.3-2     2018-01-03
##  Rsamtools                1.30.0    2017-10-31
##  RSQLite                  2.0       2017-06-19
##  rstudioapi               0.7       2017-09-07
##  rtracklayer              1.38.3    2018-01-23
##  rvest                    0.3.2     2016-06-17
##  S4Vectors              * 0.16.0    2017-10-31
##  scales                   0.5.0     2017-08-24
##  shape                    1.4.3     2017-08-16
##  shiny                    1.0.5     2017-08-23
##  SparseM                * 1.77      2017-04-23
##  splines                  3.4.3     2017-12-07
##  stats                  * 3.4.3     2017-12-07
##  stats4                 * 3.4.3     2017-12-07
##  stringi                  1.1.6     2017-11-17
##  stringr                * 1.2.0     2017-02-18
##  SummarizedExperiment   * 1.8.1     2017-12-19
##  survival                 2.41-3    2017-04-04
##  tibble                 * 1.4.2     2018-01-22
##  tidyr                  * 0.8.0     2018-01-29
##  tidyverse              * 1.2.1     2017-11-14
##  tools                    3.4.3     2017-12-07
##  tximport                 1.6.0     2017-10-31
##  utils                  * 3.4.3     2017-12-07
##  viridis                  0.4.1     2018-01-08
##  viridisLite              0.2.0     2017-03-24
##  vsn                      3.46.0    2017-10-31
##  withr                    2.1.1     2017-12-19
##  XML                      3.98-1.9  2017-06-19
##  xml2                     1.2.0     2018-01-24
##  xtable                   1.8-2     2016-02-05
##  XVector                  0.18.0    2017-10-31
##  yaml                     2.1.16    2017-12-12
##  zlibbioc                 1.24.0    2017-10-31
##  source                              
##  cran (@1.4.1)                       
##  cran (@1.56.0)                      
##  cran (@1.48.0)                      
##  Bioconductor                        
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##  CRAN (R 3.4.0)                      
##  Github (steinbaugh/basejump@265d3ce)
##  Github (hbc/bcbioBase@dc61e83)      
##  Github (hbc/bcbioRNASeq@f44ad74)    
##  CRAN (R 3.4.0)                      
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##  Bioconductor                        
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##  Bioconductor                        
##  Bioconductor                        
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##  Bioconductor                        
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##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  cran (@0.8.7)                       
##  CRAN (R 3.4.3)                      
##  cran (@0.2.15)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  cran (@0.10.13)                     
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.30.0)                      
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.1)                      
##  cran (@1.38.3)                      
##  CRAN (R 3.4.0)                      
##  Bioconductor                        
##  CRAN (R 3.4.1)                      
##  cran (@1.4.3)                       
##  cran (@1.0.5)                       
##  cran (@1.77)                        
##  local                               
##  local                               
##  local                               
##  CRAN (R 3.4.2)                      
##  CRAN (R 3.4.0)                      
##  cran (@1.8.1)                       
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  local                               
##  cran (@1.6.0)                       
##  local                               
##  cran (@0.4.1)                       
##  CRAN (R 3.4.0)                      
##  cran (@3.46.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  cran (@0.18.0)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.24.0)
> mdHeader("`utils::sessionInfo()`", level = 2)

utils::sessionInfo()

> sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2               forcats_0.2.0             
##  [3] stringr_1.2.0              dplyr_0.7.4               
##  [5] purrr_0.2.4                readr_1.1.1               
##  [7] tidyr_0.8.0                tibble_1.4.2              
##  [9] ggplot2_2.2.1              tidyverse_1.2.1           
## [11] knitr_1.19                 bcbioRNASeq_0.1.4         
## [13] DEGreport_1.14.1           quantreg_5.34             
## [15] SparseM_1.77               bcbioBase_0.0.3           
## [17] DESeq2_1.18.1              SummarizedExperiment_1.8.1
## [19] DelayedArray_0.4.1         matrixStats_0.53.0        
## [21] Biobase_2.38.0             GenomicRanges_1.30.1      
## [23] GenomeInfoDb_1.14.0        IRanges_2.12.0            
## [25] S4Vectors_0.16.0           BiocGenerics_0.24.0       
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.0.0                  backports_1.1.2              
##   [3] circlize_0.4.3                Hmisc_4.1-1                  
##   [5] AnnotationHub_2.10.1          plyr_1.8.4                   
##   [7] ConsensusClusterPlus_1.42.0   lazyeval_0.2.1               
##   [9] splines_3.4.3                 BiocParallel_1.12.0          
##  [11] digest_0.6.15                 BiocInstaller_1.28.0         
##  [13] ensembldb_2.2.0               htmltools_0.3.6              
##  [15] viridis_0.4.1                 magrittr_1.5                 
##  [17] checkmate_1.8.5               memoise_1.1.0                
##  [19] cluster_2.0.6                 limma_3.34.6                 
##  [21] ComplexHeatmap_1.17.1         Biostrings_2.46.0            
##  [23] annotate_1.56.1               Nozzle.R1_1.1-1              
##  [25] modelr_0.1.1                  R.utils_2.6.0                
##  [27] prettyunits_1.0.2             colorspace_1.3-2             
##  [29] rvest_0.3.2                   blob_1.1.0                   
##  [31] ggrepel_0.7.0                 haven_1.1.1                  
##  [33] crayon_1.3.4                  jsonlite_1.5                 
##  [35] tximport_1.6.0                RCurl_1.95-4.10              
##  [37] genefilter_1.60.0             bindr_0.1                    
##  [39] survival_2.41-3               glue_1.2.0                   
##  [41] gtable_0.2.0                  zlibbioc_1.24.0              
##  [43] XVector_0.18.0                MatrixModels_0.4-1           
##  [45] GetoptLong_0.1.6              shape_1.4.3                  
##  [47] scales_0.5.0                  vsn_3.46.0                   
##  [49] pheatmap_1.0.8                DBI_0.7                      
##  [51] edgeR_3.20.7                  Rcpp_0.12.15                 
##  [53] viridisLite_0.2.0             xtable_1.8-2                 
##  [55] progress_1.1.2                htmlTable_1.11.2             
##  [57] foreign_0.8-69                bit_1.1-12                   
##  [59] preprocessCore_1.40.0         Formula_1.2-2                
##  [61] htmlwidgets_1.0               httr_1.3.1                   
##  [63] RColorBrewer_1.1-2            acepack_1.4.1                
##  [65] reshape_0.8.7                 pkgconfig_2.0.1              
##  [67] XML_3.98-1.9                  R.methodsS3_1.7.1            
##  [69] nnet_7.3-12                   locfit_1.5-9.1               
##  [71] labeling_0.3                  reshape2_1.4.3               
##  [73] rlang_0.1.6                   AnnotationDbi_1.40.0         
##  [75] munsell_0.4.3                 cellranger_1.1.0             
##  [77] tools_3.4.3                   cli_1.0.0                    
##  [79] RSQLite_2.0                   devtools_1.13.4              
##  [81] broom_0.4.3                   evaluate_0.10.1              
##  [83] yaml_2.1.16                   bit64_0.9-7                  
##  [85] AnnotationFilter_1.2.0        nlme_3.1-131                 
##  [87] mime_0.5                      formatR_1.5                  
##  [89] R.oo_1.21.0                   grr_0.9.5                    
##  [91] xml2_1.2.0                    biomaRt_2.34.2               
##  [93] compiler_3.4.3                rstudioapi_0.7               
##  [95] curl_3.1                      interactiveDisplayBase_1.16.0
##  [97] affyio_1.48.0                 geneplotter_1.56.0           
##  [99] stringi_1.1.6                 highr_0.6                    
## [101] GenomicFeatures_1.30.1        lattice_0.20-35              
## [103] ProtGenerics_1.10.0           Matrix_1.2-12                
## [105] psych_1.7.8                   pillar_1.1.0                 
## [107] GlobalOptions_0.0.12          data.table_1.10.4-3          
## [109] cowplot_0.9.2                 bitops_1.0-6                 
## [111] Matrix.utils_0.9.6            httpuv_1.3.5                 
## [113] rtracklayer_1.38.3            affy_1.56.0                  
## [115] R6_2.2.2                      latticeExtra_0.6-28          
## [117] RMySQL_0.10.13                gridExtra_2.3                
## [119] codetools_0.2-15              assertthat_0.2.0             
## [121] rprojroot_1.3-2               rjson_0.2.15                 
## [123] withr_2.1.1                   mnormt_1.5-5                 
## [125] GenomicAlignments_1.14.1      Rsamtools_1.30.0             
## [127] GenomeInfoDbData_1.0.0        hms_0.4.1                    
## [129] grid_3.4.3                    rpart_4.1-12                 
## [131] rmarkdown_1.8                 dendsort_0.3.3               
## [133] logging_0.7-103               lubridate_1.7.1              
## [135] shiny_1.0.5                   base64enc_0.1-3              
## [137] basejump_0.2.0
> mdHeader("YAML params", level = 2)

YAML params

> print(params)
## $bcbFile
## [1] "data/bcb_sub_lung.rda"
## 
## $design
## ~prepBatch + sampleclass
## <environment: 0x7fdd1526ea30>
## 
## $contrast
## [1] "sampleclass"      "lung_distaltumor" "lung_notumor"    
## 
## $alpha
## [1] 0.05
## 
## $lfc
## [1] 1
## 
## $outputDir
## [1] "."

Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1). [Royal Statistical Society, Wiley]: 289–300. http://www.jstor.org/stable/2346101.

Cui, Xiangqin, and Gary A Churchill. 2003. “Statistical Tests for Differential Expression in cDNA Microarray Experiments.” Genome Biol. 4 (4): 210. https://www.ncbi.nlm.nih.gov/pubmed/12702200.

Kolde, Raivo. 2015. Pheatmap: Pretty Heatmaps. https://CRAN.R-project.org/package=pheatmap.

Li, Wentian, Jan Freudenberg, Young Ju Suh, and Yaning Yang. 2014. “Using Volcano Plots and Regularized-Chi Statistics in Genetic Association Studies.” Comput. Biol. Chem. 48 (February): 77–83. doi:10.1016/j.compbiolchem.2013.02.003.

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12): 550. doi:10.1186/s13059-014-0550-8.

Patro, Rob, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nat. Methods 14 (4): 417–19. doi:10.1038/nmeth.4197.

Soneson, Charlotte, Michael I Love, and Mark D Robinson. 2016. “Differential Analyses for RNA-seq: Transcript-Level Estimates Improve Gene-Level Inferences.” F1000Res. 4 (December). doi:10.12688/f1000research.7563.1.

Ward, Joe H, Jr. 1963. “Hierarchical Grouping to Optimize an Objective Function.” Journal of the American Statistical Association 58 (301). Taylor & Francis: 236–44. doi:10.1080/01621459.1963.10500845.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Use R. Springer New York. doi:10.1007/978-0-387-98141-3.

Yang, Yee Hwa, Sandrine Dudoit, Percy Luu, David M Lin, Vivian Peng, John Ngai, and Terence P Speed. 2002. “Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation.” Nucleic Acids Res. 30 (4): e15. https://www.ncbi.nlm.nih.gov/pubmed/11842121.